Cohort 2 Taxonomy
Liz Mallott
1/21/2021
Overview of analysis
This analysis examines if overall community composition differs based on self-reported ethnicity and what specific taxa are differentially abundant. MetaPhlAn (v3.0.7) was used to profile the taxonomic composition of metagenomes and calculate unweighted and weighted UniFrac distances.
Setting up the environment
Load the necessary libraries.
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.4 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.0## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(vegan)## Loading required package: permute## Loading required package: lattice## This is vegan 2.5-7library(ggplot2)
library(cowplot)Importing data
Import metadata
metadata = read_tsv("year2_sample_mapping_metaphlan.tsv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_character(),
## seq_id_simple = col_double(),
## fecal_use = col_double(),
## oral_use = col_double(),
## mock = col_double(),
## blank = col_double(),
## day_number = col_double(),
## time = col_time(format = ""),
## extraction_kit_use = col_double(),
## extraction = col_double(),
## library_plate = col_double(),
## id_num = col_double()
## )
## ℹ Use `spec()` for the full column specifications.Import species-level relative abundance table, transpose, and recalculate relative abundance to sum to 1 instead of 100.
relab = read_tsv("year2_merged_abundance_table_species.txt")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## body_site = col_character()
## )
## ℹ Use `spec()` for the full column specifications.relab1 = relab %>% mutate_if(is.numeric, ~./100)
relab1_t = relab1 %>% gather(file, count, 2:164) %>%
spread(body_site, count)Import UniFrac matrices
wuni_fecal = as.dist(
read.table("year2_merged_abundance_wunifrac_fecal.tsv"))
uuni_fecal = as.dist(
read.table("year2_merged_abundance_uunifrac_fecal.tsv"))
wuni_oral = as.dist(
read.table("year2_merged_abundance_wunifrac_oral.tsv"))
uuni_oral = as.dist(
read.table("year2_merged_abundance_uunifrac_oral.tsv"))
wuni_fecalb = as.dist(
read.table("year2_merged_abundance_wunifrac_fecalb.tsv"))
uuni_fecalb = as.dist(
read.table("year2_merged_abundance_uunifrac_fecalb.tsv"))
wuni_oralb = as.dist(
read.table("year2_merged_abundance_wunifrac_oralb.tsv"))
uuni_oralb = as.dist(
read.table("year2_merged_abundance_uunifrac_oralb.tsv"))
wuni_fecala = as.dist(
read.table("year2_merged_abundance_wunifrac_fecala.tsv"))
uuni_fecala = as.dist(
read.table("year2_merged_abundance_uunifrac_fecala.tsv"))
wuni_orala = as.dist(
read.table("year2_merged_abundance_wunifrac_orala.tsv"))
uuni_orala = as.dist(
read.table("year2_merged_abundance_uunifrac_orala.tsv"))
wuni_fecald = as.dist(
read.table("year2_merged_abundance_wunifrac_fecald.tsv"))
uuni_fecald = as.dist(
read.table("year2_merged_abundance_uunifrac_fecald.tsv"))Filter samples
Filter out samples that have no identified species or are controls (blanks and mock communities). Separate oral samples from fecal samples.
relab_fecal = relab1_t %>% left_join(metadata, by = "file") %>%
filter(source == "fecal")
relab_fecal_filtered = relab_fecal %>%
mutate(total = rowSums(relab_fecal[,2:496])) %>%
filter(total > 0)
relab_oral = relab1_t %>% left_join(metadata, by = "file") %>%
filter(source == "oral")Calculating beta diversity metrics
UniFrac distances are calculated by a MetaPhlAn helper function. Vegan is used to calculate the Bray-Curtis dissimilarity and Jaccard similarity metrics.
brayf = vegdist(relab_fecal_filtered[,2:496], method = "bray")
jaccf = vegdist(relab_fecal_filtered[,2:496], method = "jaccard")
brayo = vegdist(relab_oral[,2:496], method = "bray")
jacco = vegdist(relab_oral[,2:496], method = "jaccard")PERMANOVAs - All Samples
Differences in the taxonomic structure of the community associated with self-reported ethnicity is assessed using permutational multivariate analysis of variance.
Bray-Curtis
Fecal
adonis2(brayf ~ ethnicity, data = relab_fecal_filtered,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = brayf ~ ethnicity, data = relab_fecal_filtered, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 1.4525 0.06536 7.6225 2e-04 ***
## Residual 109 20.7702 0.93464
## Total 110 22.2227 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Oral
adonis2(brayo ~ ethnicity, data = relab_oral,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = brayo ~ ethnicity, data = relab_oral, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.3486 0.0908 3.1957 0.0042 **
## Residual 32 3.4905 0.9092
## Total 33 3.8391 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1NMDS visualization of Bray-Curtis dissimilarity with all samples.
## Run 0 stress 0.1730196
## Run 1 stress 0.1954393
## Run 2 stress 0.1890694
## Run 3 stress 0.1986366
## Run 4 stress 0.2163515
## Run 5 stress 0.1730196
## ... New best solution
## ... Procrustes: rmse 2.113784e-05 max resid 9.989284e-05
## ... Similar to previous best
## Run 6 stress 0.1857489
## Run 7 stress 0.1730197
## ... Procrustes: rmse 9.043795e-05 max resid 0.000386343
## ... Similar to previous best
## Run 8 stress 0.2305086
## Run 9 stress 0.2132002
## Run 10 stress 0.2292344
## Run 11 stress 0.1878283
## Run 12 stress 0.1875348
## Run 13 stress 0.2040644
## Run 14 stress 0.2066121
## Run 15 stress 0.1857489
## Run 16 stress 0.2037089
## Run 17 stress 0.1931462
## Run 18 stress 0.17302
## ... Procrustes: rmse 0.0001923967 max resid 0.0007958677
## ... Similar to previous best
## Run 19 stress 0.2195464
## Run 20 stress 0.2084767
## *** Solution reached
## Run 0 stress 0.1592337
## Run 1 stress 0.2032347
## Run 2 stress 0.1592335
## ... New best solution
## ... Procrustes: rmse 0.000139033 max resid 0.0006605158
## ... Similar to previous best
## Run 3 stress 0.1754509
## Run 4 stress 0.1591203
## ... New best solution
## ... Procrustes: rmse 0.02988077 max resid 0.1460053
## Run 5 stress 0.1994552
## Run 6 stress 0.1591203
## ... Procrustes: rmse 2.196014e-05 max resid 9.723075e-05
## ... Similar to previous best
## Run 7 stress 0.1665592
## Run 8 stress 0.1884425
## Run 9 stress 0.1629265
## Run 10 stress 0.1660616
## Run 11 stress 0.1665592
## Run 12 stress 0.1937613
## Run 13 stress 0.1632234
## Run 14 stress 0.1629265
## Run 15 stress 0.1673417
## Run 16 stress 0.158507
## ... New best solution
## ... Procrustes: rmse 0.03767336 max resid 0.1911403
## Run 17 stress 0.1660616
## Run 18 stress 0.1766695
## Run 19 stress 0.158507
## ... Procrustes: rmse 2.419396e-05 max resid 0.0001101113
## ... Similar to previous best
## Run 20 stress 0.1591203
## *** Solution reached
tiff(file = "figures/nmds_plot_taxa_bray.tif", res = 150, width = 18,
height = 7, units = "in")
legend1 = get_legend(brayf + theme(legend.box.margin = margin(0, 0, 0, 12)))
col1 = plot_grid(brayf + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('A'),
label_size = 20)
col2 = plot_grid(brayo + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('B'),
label_size = 20)
plot = plot_grid(col1, col2, legend1, nrow = 1, ncol = 3,
rel_widths = c(1.5, 1.5, 0.75), align = "h", axis = "t")
plot
dev.off()## quartz_off_screen
## 2Jaccard
Fecal
adonis2(jaccf ~ ethnicity, data = relab_fecal_filtered,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = jaccf ~ ethnicity, data = relab_fecal_filtered, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 1.904 0.05969 6.9197 2e-04 ***
## Residual 109 29.992 0.94031
## Total 110 31.896 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Oral
adonis2(jacco ~ ethnicity, data = relab_oral,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = jacco ~ ethnicity, data = relab_oral, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.4911 0.07269 2.5084 0.0042 **
## Residual 32 6.2653 0.92731
## Total 33 6.7564 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1NMDS visualization of Jaccard similarity with all samples.
## Run 0 stress 0.173776
## Run 1 stress 0.1906485
## Run 2 stress 0.1919843
## Run 3 stress 0.201194
## Run 4 stress 0.1783047
## Run 5 stress 0.1777442
## Run 6 stress 0.1957943
## Run 7 stress 0.2099691
## Run 8 stress 0.177744
## Run 9 stress 0.2066552
## Run 10 stress 0.1730196
## ... New best solution
## ... Procrustes: rmse 0.03078324 max resid 0.1163285
## Run 11 stress 0.2185253
## Run 12 stress 0.1917683
## Run 13 stress 0.2273878
## Run 14 stress 0.1880972
## Run 15 stress 0.2022778
## Run 16 stress 0.2053309
## Run 17 stress 0.2215973
## Run 18 stress 0.2077631
## Run 19 stress 0.2043171
## Run 20 stress 0.1928452
## Run 21 stress 0.2070218
## Run 22 stress 0.2120901
## Run 23 stress 0.1838072
## Run 24 stress 0.1948416
## Run 25 stress 0.2009606
## Run 26 stress 0.177744
## Run 27 stress 0.1765942
## Run 28 stress 0.2025634
## Run 29 stress 0.1957946
## Run 30 stress 0.2033358
## Run 31 stress 0.1744768
## Run 32 stress 0.1730196
## ... New best solution
## ... Procrustes: rmse 1.478532e-05 max resid 7.113282e-05
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.1592336
## Run 1 stress 0.1820792
## Run 2 stress 0.1665592
## Run 3 stress 0.1890765
## Run 4 stress 0.1593585
## ... Procrustes: rmse 0.03276102 max resid 0.1485467
## Run 5 stress 0.1609911
## Run 6 stress 0.1660763
## Run 7 stress 0.1861276
## Run 8 stress 0.1797775
## Run 9 stress 0.1593585
## ... Procrustes: rmse 0.03276085 max resid 0.1485464
## Run 10 stress 0.1609914
## Run 11 stress 0.1591202
## ... New best solution
## ... Procrustes: rmse 0.02981504 max resid 0.1456599
## Run 12 stress 0.1754509
## Run 13 stress 0.180303
## Run 14 stress 0.158507
## ... New best solution
## ... Procrustes: rmse 0.03766872 max resid 0.1911411
## Run 15 stress 0.1629265
## Run 16 stress 0.1609932
## Run 17 stress 0.1665592
## Run 18 stress 0.1609911
## Run 19 stress 0.2058922
## Run 20 stress 0.1819535
## Run 21 stress 0.1665592
## Run 22 stress 0.1624179
## Run 23 stress 0.1927008
## Run 24 stress 0.1665592
## Run 25 stress 0.1632234
## Run 26 stress 0.1665592
## Run 27 stress 0.1805669
## Run 28 stress 0.1766695
## Run 29 stress 0.1593585
## Run 30 stress 0.162418
## Run 31 stress 0.1665592
## Run 32 stress 0.1665592
## Run 33 stress 0.1806949
## Run 34 stress 0.1593585
## Run 35 stress 0.1823253
## Run 36 stress 0.181956
## Run 37 stress 0.1629265
## Run 38 stress 0.1888587
## Run 39 stress 0.1632234
## Run 40 stress 0.1819222
## Run 41 stress 0.1766695
## Run 42 stress 0.1798959
## Run 43 stress 0.1660616
## Run 44 stress 0.201518
## Run 45 stress 0.1609928
## Run 46 stress 0.1664842
## Run 47 stress 0.1629265
## Run 48 stress 0.1632234
## Run 49 stress 0.158507
## ... New best solution
## ... Procrustes: rmse 7.061952e-06 max resid 2.066634e-05
## ... Similar to previous best
## *** Solution reached
tiff(file = "figures/nmds_plot_taxa_jacc.tif", res = 150, width = 18,
height = 7, units = "in")
legend1 = get_legend(jaccf + theme(legend.box.margin = margin(0, 0, 0, 12)))
col1 = plot_grid(jaccf + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('A'),
label_size = 20)
col2 = plot_grid(jacco + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('B'),
label_size = 20)
plot = plot_grid(col1, col2, legend1, nrow = 1, ncol = 3,
rel_widths = c(1.5, 1.5, 0.75), align = "h", axis = "t")
plot
dev.off()## quartz_off_screen
## 2Weighted UniFrac
Fecal
adonis2(wuni_fecal ~ ethnicity, data = relab_fecal_filtered,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = wuni_fecal ~ ethnicity, data = relab_fecal_filtered, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.2429 0.04942 5.6666 0.0012 **
## Residual 109 4.6721 0.95058
## Total 110 4.9150 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Oral
adonis2(wuni_oral ~ ethnicity, data = relab_oral,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = wuni_oral ~ ethnicity, data = relab_oral, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.07415 0.04607 1.5456 0.1736
## Residual 32 1.53533 0.95393
## Total 33 1.60948 1.00000NMDS visualization of weighted Unifrac distances with all samples.
## Run 0 stress 0.1128885
## Run 1 stress 0.1129679
## ... Procrustes: rmse 0.006469168 max resid 0.0638261
## Run 2 stress 0.1211153
## Run 3 stress 0.1129243
## ... Procrustes: rmse 0.006351517 max resid 0.06336234
## Run 4 stress 0.1128867
## ... New best solution
## ... Procrustes: rmse 0.0007263931 max resid 0.005928428
## ... Similar to previous best
## Run 5 stress 0.1130648
## ... Procrustes: rmse 0.01034694 max resid 0.08152708
## Run 6 stress 0.1313283
## Run 7 stress 0.1378563
## Run 8 stress 0.1128836
## ... New best solution
## ... Procrustes: rmse 0.006424424 max resid 0.06385617
## Run 9 stress 0.1129707
## ... Procrustes: rmse 0.001786909 max resid 0.01311021
## Run 10 stress 0.1163974
## Run 11 stress 0.113063
## ... Procrustes: rmse 0.007096284 max resid 0.07143052
## Run 12 stress 0.4130381
## Run 13 stress 0.1173702
## Run 14 stress 0.1129006
## ... Procrustes: rmse 0.006351807 max resid 0.06427751
## Run 15 stress 0.1421137
## Run 16 stress 0.1166752
## Run 17 stress 0.1128898
## ... Procrustes: rmse 0.0009485802 max resid 0.005774957
## ... Similar to previous best
## Run 18 stress 0.1128898
## ... Procrustes: rmse 0.006383938 max resid 0.06402909
## Run 19 stress 0.1326057
## Run 20 stress 0.125718
## *** Solution reached
## Run 0 stress 0.08960119
## Run 1 stress 0.08968849
## ... Procrustes: rmse 0.01592553 max resid 0.0774785
## Run 2 stress 0.1025332
## Run 3 stress 0.1085128
## Run 4 stress 0.08968851
## ... Procrustes: rmse 0.01592196 max resid 0.07746427
## Run 5 stress 0.103023
## Run 6 stress 0.1178927
## Run 7 stress 0.1025332
## Run 8 stress 0.08968849
## ... Procrustes: rmse 0.01592304 max resid 0.07746805
## Run 9 stress 0.0983816
## Run 10 stress 0.1025332
## Run 11 stress 0.1157697
## Run 12 stress 0.1082859
## Run 13 stress 0.0896012
## ... Procrustes: rmse 3.168302e-05 max resid 0.0001261021
## ... Similar to previous best
## Run 14 stress 0.1000692
## Run 15 stress 0.08968849
## ... Procrustes: rmse 0.01592722 max resid 0.0774915
## Run 16 stress 0.1084938
## Run 17 stress 0.1062022
## Run 18 stress 0.1172026
## Run 19 stress 0.1000692
## Run 20 stress 0.1086048
## *** Solution reached
tiff(file = "figures/nmds_plot_taxa_wuni.tif", res = 150, width = 18,
height = 7, units = "in")
legend1 = get_legend(wunif + theme(legend.box.margin = margin(0, 0, 0, 12)))
col1 = plot_grid(wunif + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('A'),
label_size = 20)
col2 = plot_grid(wunio + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('B'),
label_size = 20)
plot = plot_grid(col1, col2, legend1, nrow = 1, ncol = 3,
rel_widths = c(1.5, 1.5, 0.75), align = "h", axis = "t")
plot
dev.off()## quartz_off_screen
## 2Unweighted UniFrac
Fecal
adonis2(uuni_fecal ~ ethnicity, data = relab_fecal_filtered,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = uuni_fecal ~ ethnicity, data = relab_fecal_filtered, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.7732 0.07238 8.5048 2e-04 ***
## Residual 109 9.9100 0.92762
## Total 110 10.6832 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Oral
adonis2(uuni_oral ~ ethnicity, data = relab_oral,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = uuni_oral ~ ethnicity, data = relab_oral, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.05327 0.0402 1.3401 0.1916
## Residual 32 1.27193 0.9598
## Total 33 1.32520 1.0000NMDS visualization of Bray-Curtis dissimilarity with all samples.
## Run 0 stress 7.26794e-05
## Run 1 stress 8.89407e-05
## ... Procrustes: rmse 2.6563e-05 max resid 0.0001140349
## ... Similar to previous best
## Run 2 stress 9.419245e-05
## ... Procrustes: rmse 4.891671e-05 max resid 0.0001255239
## ... Similar to previous best
## Run 3 stress 8.555324e-05
## ... Procrustes: rmse 2.615283e-05 max resid 0.0001006979
## ... Similar to previous best
## Run 4 stress 8.908464e-05
## ... Procrustes: rmse 3.695045e-05 max resid 0.0001250117
## ... Similar to previous best
## Run 5 stress 8.083382e-05
## ... Procrustes: rmse 3.973176e-05 max resid 0.0001347788
## ... Similar to previous best
## Run 6 stress 9.079105e-05
## ... Procrustes: rmse 3.511466e-05 max resid 0.0001306709
## ... Similar to previous best
## Run 7 stress 9.147368e-05
## ... Procrustes: rmse 2.977313e-05 max resid 0.0001073563
## ... Similar to previous best
## Run 8 stress 7.159914e-05
## ... New best solution
## ... Procrustes: rmse 2.972201e-05 max resid 0.0001033728
## ... Similar to previous best
## Run 9 stress 8.509091e-05
## ... Procrustes: rmse 3.107911e-05 max resid 9.833484e-05
## ... Similar to previous best
## Run 10 stress 7.854869e-05
## ... Procrustes: rmse 2.14154e-05 max resid 7.716503e-05
## ... Similar to previous best
## Run 11 stress 8.615579e-05
## ... Procrustes: rmse 2.458636e-05 max resid 8.14055e-05
## ... Similar to previous best
## Run 12 stress 7.366728e-05
## ... Procrustes: rmse 1.979365e-05 max resid 9.986138e-05
## ... Similar to previous best
## Run 13 stress 9.662353e-05
## ... Procrustes: rmse 2.424657e-05 max resid 6.917625e-05
## ... Similar to previous best
## Run 14 stress 9.077723e-05
## ... Procrustes: rmse 4.125896e-05 max resid 0.000110526
## ... Similar to previous best
## Run 15 stress 6.001949e-05
## ... New best solution
## ... Procrustes: rmse 2.02629e-05 max resid 6.462539e-05
## ... Similar to previous best
## Run 16 stress 7.398229e-05
## ... Procrustes: rmse 1.990127e-05 max resid 6.056812e-05
## ... Similar to previous best
## Run 17 stress 7.391904e-05
## ... Procrustes: rmse 2.237521e-05 max resid 6.279285e-05
## ... Similar to previous best
## Run 18 stress 9.026151e-05
## ... Procrustes: rmse 3.250961e-05 max resid 7.56077e-05
## ... Similar to previous best
## Run 19 stress 9.061626e-05
## ... Procrustes: rmse 2.611029e-05 max resid 0.0001082571
## ... Similar to previous best
## Run 20 stress 8.268287e-05
## ... Procrustes: rmse 2.122476e-05 max resid 6.927945e-05
## ... Similar to previous best
## *** Solution reached## Warning in metaMDS(uuni_fecal, k = 2, trymax = 500): stress is (nearly) zero:
## you may have insufficient data## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
## Run 0 stress 0.1791554
## Run 1 stress 0.1819031
## Run 2 stress 0.1879203
## Run 3 stress 0.1834742
## Run 4 stress 0.1833687
## Run 5 stress 0.1830513
## Run 6 stress 0.1825586
## Run 7 stress 0.2191732
## Run 8 stress 0.1933048
## Run 9 stress 0.1806554
## Run 10 stress 0.193551
## Run 11 stress 0.1815046
## Run 12 stress 0.1791029
## ... New best solution
## ... Procrustes: rmse 0.007986632 max resid 0.03645454
## Run 13 stress 0.1791029
## ... New best solution
## ... Procrustes: rmse 3.331413e-05 max resid 9.393246e-05
## ... Similar to previous best
## Run 14 stress 0.1795051
## ... Procrustes: rmse 0.01244262 max resid 0.05107072
## Run 15 stress 0.1791029
## ... Procrustes: rmse 5.581876e-05 max resid 0.0001815511
## ... Similar to previous best
## Run 16 stress 0.1909943
## Run 17 stress 0.1873035
## Run 18 stress 0.1792178
## ... Procrustes: rmse 0.01941215 max resid 0.053807
## Run 19 stress 0.1814713
## Run 20 stress 0.1791554
## ... Procrustes: rmse 0.007993025 max resid 0.03651193
## *** Solution reached
tiff(file = "figures/nmds_plot_taxa_uuni.tif", res = 150, width = 18,
height = 7, units = "in")
legend1 = get_legend(uunif + theme(legend.box.margin = margin(0, 0, 0, 12)))## Warning in MASS::cov.trob(data[, vars]): Probable convergence failurecol1 = plot_grid(uunif + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('A'),
label_size = 20)## Warning in MASS::cov.trob(data[, vars]): Probable convergence failurecol2 = plot_grid(uunio + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('B'),
label_size = 20)
plot = plot_grid(col1, col2, legend1, nrow = 1, ncol = 3,
rel_widths = c(1.5, 1.5, 0.75), align = "h", axis = "t")
plot
dev.off()## quartz_off_screen
## 2PERMANOVAs - Before samples
Filtering samples
Filter samples from before the diet intervention and recalculate Bray-Curtis and Jaccard metrics
relab_fecal_filtered_before = relab_fecal_filtered %>%
filter(Study_period == "Before")
relab_oral_before = relab_oral %>%
filter(Study_period == "Before")
brayfb = vegdist(relab_fecal_filtered_before[,2:496], method = "bray")
jaccfb = vegdist(relab_fecal_filtered_before[,2:496], method = "jaccard")
brayob = vegdist(relab_oral_before[,2:496], method = "bray")
jaccob = vegdist(relab_oral_before[,2:496], method = "jaccard")Bray-Curtis
Fecal
adonis2(brayfb ~ ethnicity, data = relab_fecal_filtered_before,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = brayfb ~ ethnicity, data = relab_fecal_filtered_before, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.2744 0.06875 1.255 0.2314
## Residual 17 3.7171 0.93125
## Total 18 3.9915 1.00000Oral
adonis2(brayob ~ ethnicity, data = relab_oral_before,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = brayob ~ ethnicity, data = relab_oral_before, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.18873 0.09118 1.505 0.1208
## Residual 15 1.88112 0.90882
## Total 16 2.06985 1.00000NMDS visualization of Bray-Curtis dissimilarity with before samples.
## Run 0 stress 0.1284112
## Run 1 stress 0.1712267
## Run 2 stress 0.1444672
## Run 3 stress 0.1284112
## ... Procrustes: rmse 5.107342e-06 max resid 1.072505e-05
## ... Similar to previous best
## Run 4 stress 0.1612495
## Run 5 stress 0.1284112
## ... Procrustes: rmse 5.270567e-06 max resid 1.099011e-05
## ... Similar to previous best
## Run 6 stress 0.1284112
## ... New best solution
## ... Procrustes: rmse 1.868258e-06 max resid 3.636777e-06
## ... Similar to previous best
## Run 7 stress 0.1617329
## Run 8 stress 0.1842236
## Run 9 stress 0.2014128
## Run 10 stress 0.1444672
## Run 11 stress 0.1506507
## Run 12 stress 0.1712268
## Run 13 stress 0.1712267
## Run 14 stress 0.1444672
## Run 15 stress 0.1792601
## Run 16 stress 0.158196
## Run 17 stress 0.1618869
## Run 18 stress 0.1842236
## Run 19 stress 0.1525329
## Run 20 stress 0.158196
## *** Solution reached
## Run 0 stress 0.1253357
## Run 1 stress 0.1481009
## Run 2 stress 0.1453195
## Run 3 stress 0.1364623
## Run 4 stress 0.1585964
## Run 5 stress 0.1591175
## Run 6 stress 0.1453195
## Run 7 stress 0.1585963
## Run 8 stress 0.1498923
## Run 9 stress 0.1453195
## Run 10 stress 0.1591175
## Run 11 stress 0.1481009
## Run 12 stress 0.1403222
## Run 13 stress 0.1300515
## Run 14 stress 0.1405455
## Run 15 stress 0.1364625
## Run 16 stress 0.3689216
## Run 17 stress 0.1253357
## ... Procrustes: rmse 0.0001814997 max resid 0.0005916947
## ... Similar to previous best
## Run 18 stress 0.1253358
## ... Procrustes: rmse 0.0002713796 max resid 0.0008824368
## ... Similar to previous best
## Run 19 stress 0.1686967
## Run 20 stress 0.1467916
## *** Solution reached
tiff(file = "figures/nmds_plot_taxa_bray_before.tif", res = 150, width = 18,
height = 7, units = "in")
legend1 = get_legend(brayfb + theme(legend.box.margin = margin(0, 0, 0, 12)))
col1 = plot_grid(brayfb + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('A'),
label_size = 20)
col2 = plot_grid(brayob + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('B'),
label_size = 20)
plot = plot_grid(col1, col2, legend1, nrow = 1, ncol = 3,
rel_widths = c(1.5, 1.5, 0.75), align = "h", axis = "t")
plot
dev.off()## quartz_off_screen
## 2Jaccard
Fecal
adonis2(jaccfb ~ ethnicity, data = relab_fecal_filtered_before,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = jaccfb ~ ethnicity, data = relab_fecal_filtered_before, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.4122 0.07421 1.3628 0.1328
## Residual 17 5.1416 0.92579
## Total 18 5.5537 1.00000Oral
adonis2(jaccob ~ ethnicity, data = relab_oral_before,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = jaccob ~ ethnicity, data = relab_oral_before, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.2815 0.07968 1.2988 0.1338
## Residual 15 3.2508 0.92032
## Total 16 3.5323 1.00000NMDS visualization of Jaccard similarity with before samples.
## Run 0 stress 0.1284112
## Run 1 stress 0.1444672
## Run 2 stress 0.161887
## Run 3 stress 0.1518819
## Run 4 stress 0.1284112
## ... Procrustes: rmse 4.827122e-06 max resid 9.395989e-06
## ... Similar to previous best
## Run 5 stress 0.1518819
## Run 6 stress 0.1284112
## ... Procrustes: rmse 2.297949e-06 max resid 4.477037e-06
## ... Similar to previous best
## Run 7 stress 0.162817
## Run 8 stress 0.1284112
## ... Procrustes: rmse 3.074062e-06 max resid 6.583554e-06
## ... Similar to previous best
## Run 9 stress 0.1581961
## Run 10 stress 0.1842236
## Run 11 stress 0.1617329
## Run 12 stress 0.2626449
## Run 13 stress 0.1284112
## ... Procrustes: rmse 3.799604e-06 max resid 7.560866e-06
## ... Similar to previous best
## Run 14 stress 0.1612495
## Run 15 stress 0.1284112
## ... Procrustes: rmse 2.600561e-06 max resid 5.256494e-06
## ... Similar to previous best
## Run 16 stress 0.1791334
## Run 17 stress 0.1792602
## Run 18 stress 0.1712269
## Run 19 stress 0.1525329
## Run 20 stress 0.1284112
## ... Procrustes: rmse 4.904981e-06 max resid 1.056895e-05
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.1253358
## Run 1 stress 0.1591172
## Run 2 stress 0.1364623
## Run 3 stress 0.1455176
## Run 4 stress 0.1295583
## Run 5 stress 0.1689227
## Run 6 stress 0.1253357
## ... New best solution
## ... Procrustes: rmse 0.0001331197 max resid 0.0004425188
## ... Similar to previous best
## Run 7 stress 0.1256166
## ... Procrustes: rmse 0.02144201 max resid 0.0619013
## Run 8 stress 0.1461135
## Run 9 stress 0.1295583
## Run 10 stress 0.1405455
## Run 11 stress 0.1384923
## Run 12 stress 0.1453195
## Run 13 stress 0.3633597
## Run 14 stress 0.1403222
## Run 15 stress 0.1364623
## Run 16 stress 0.1300761
## Run 17 stress 0.1591176
## Run 18 stress 0.1484361
## Run 19 stress 0.1364624
## Run 20 stress 0.1467916
## *** Solution reached
tiff(file = "figures/nmds_plot_taxa_jacc_before.tif", res = 150, width = 18,
height = 7, units = "in")
legend1 = get_legend(jaccfb + theme(legend.box.margin = margin(0, 0, 0, 12)))
col1 = plot_grid(jaccfb + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('A'),
label_size = 20)
col2 = plot_grid(jaccob + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('B'),
label_size = 20)
plot = plot_grid(col1, col2, legend1, nrow = 1, ncol = 3,
rel_widths = c(1.5, 1.5, 0.75), align = "h", axis = "t")
plot
dev.off()## quartz_off_screen
## 2Weighted UniFrac
Fecal
adonis2(wuni_fecalb ~ ethnicity, data = relab_fecal_filtered_before,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = wuni_fecalb ~ ethnicity, data = relab_fecal_filtered_before, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.02942 0.03414 0.6009 0.6444
## Residual 17 0.83220 0.96586
## Total 18 0.86162 1.00000Oral
adonis2(wuni_oralb ~ ethnicity, data = relab_oral_before,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = wuni_oralb ~ ethnicity, data = relab_oral_before, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.04241 0.04763 0.7501 0.4686
## Residual 15 0.84808 0.95237
## Total 16 0.89049 1.00000NMDS visualization of weighted Unifrac distances with before samples.
## Run 0 stress 0.07991057
## Run 1 stress 0.2321205
## Run 2 stress 0.08273949
## Run 3 stress 0.07999755
## ... Procrustes: rmse 0.009288069 max resid 0.02907652
## Run 4 stress 0.08403423
## Run 5 stress 0.08148164
## Run 6 stress 0.1424107
## Run 7 stress 0.08148173
## Run 8 stress 0.1475728
## Run 9 stress 0.08273945
## Run 10 stress 0.07991036
## ... New best solution
## ... Procrustes: rmse 0.0004527546 max resid 0.001336798
## ... Similar to previous best
## Run 11 stress 0.08148166
## Run 12 stress 0.08273955
## Run 13 stress 0.08273944
## Run 14 stress 0.07999764
## ... Procrustes: rmse 0.009378223 max resid 0.02906354
## Run 15 stress 0.08403422
## Run 16 stress 0.07991039
## ... Procrustes: rmse 0.0003635338 max resid 0.00100614
## ... Similar to previous best
## Run 17 stress 0.08273945
## Run 18 stress 0.08273949
## Run 19 stress 0.1453388
## Run 20 stress 0.08403423
## *** Solution reached
## Run 0 stress 0.06894997
## Run 1 stress 0.06894997
## ... New best solution
## ... Procrustes: rmse 8.094821e-06 max resid 2.596814e-05
## ... Similar to previous best
## Run 2 stress 0.07415714
## Run 3 stress 0.06966296
## Run 4 stress 0.07415714
## Run 5 stress 0.08144912
## Run 6 stress 0.06966296
## Run 7 stress 0.06966296
## Run 8 stress 0.0761536
## Run 9 stress 0.08571785
## Run 10 stress 0.07047078
## Run 11 stress 0.06894997
## ... New best solution
## ... Procrustes: rmse 5.478821e-06 max resid 1.561733e-05
## ... Similar to previous best
## Run 12 stress 0.0704708
## Run 13 stress 0.07643534
## Run 14 stress 0.07415715
## Run 15 stress 0.07415714
## Run 16 stress 0.07643538
## Run 17 stress 0.06894998
## ... Procrustes: rmse 3.032653e-05 max resid 9.395695e-05
## ... Similar to previous best
## Run 18 stress 0.285
## Run 19 stress 0.06894998
## ... Procrustes: rmse 1.473e-05 max resid 3.59209e-05
## ... Similar to previous best
## Run 20 stress 0.06894997
## ... Procrustes: rmse 6.664391e-06 max resid 1.804576e-05
## ... Similar to previous best
## *** Solution reached
tiff(file = "figures/nmds_plot_taxa_wuni_before.tif", res = 150, width = 18,
height = 7, units = "in")
legend1 = get_legend(wunifb + theme(legend.box.margin = margin(0, 0, 0, 12)))
col1 = plot_grid(wunifb + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('A'),
label_size = 20)
col2 = plot_grid(wuniob + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('B'),
label_size = 20)
plot = plot_grid(col1, col2, legend1, nrow = 1, ncol = 3,
rel_widths = c(1.5, 1.5, 0.75), align = "h", axis = "t")
plot
dev.off()## quartz_off_screen
## 2Unweighted UniFrac
Fecal
adonis2(uuni_fecalb ~ ethnicity, data = relab_fecal_filtered_before,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = uuni_fecalb ~ ethnicity, data = relab_fecal_filtered_before, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.18751 0.08793 1.6389 0.0182 *
## Residual 17 1.94502 0.91207
## Total 18 2.13253 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Oral
adonis2(uuni_oralb ~ ethnicity, data = relab_oral_before,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = uuni_oralb ~ ethnicity, data = relab_oral_before, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.03204 0.04827 0.7608 0.7076
## Residual 15 0.63162 0.95173
## Total 16 0.66365 1.00000NMDS visualization of unweighted UniFrac distances with before samples.
## Run 0 stress 9.874988e-05
## Run 1 stress 9.961836e-05
## ... Procrustes: rmse 4.474348e-05 max resid 8.783092e-05
## ... Similar to previous best
## Run 2 stress 7.274105e-05
## ... New best solution
## ... Procrustes: rmse 0.0001072195 max resid 0.00024281
## ... Similar to previous best
## Run 3 stress 8.935065e-05
## ... Procrustes: rmse 9.023071e-05 max resid 0.000223894
## ... Similar to previous best
## Run 4 stress 9.359378e-05
## ... Procrustes: rmse 0.0001046243 max resid 0.0002039388
## ... Similar to previous best
## Run 5 stress 9.251685e-05
## ... Procrustes: rmse 0.0001186332 max resid 0.00025548
## ... Similar to previous best
## Run 6 stress 9.371519e-05
## ... Procrustes: rmse 0.0001033606 max resid 0.0002122299
## ... Similar to previous best
## Run 7 stress 9.45746e-05
## ... Procrustes: rmse 0.0001015814 max resid 0.0002177958
## ... Similar to previous best
## Run 8 stress 8.808076e-05
## ... Procrustes: rmse 8.321261e-05 max resid 0.000185678
## ... Similar to previous best
## Run 9 stress 8.642016e-05
## ... Procrustes: rmse 5.583815e-05 max resid 0.0001016477
## ... Similar to previous best
## Run 10 stress 9.755127e-05
## ... Procrustes: rmse 9.972745e-05 max resid 0.0002585689
## ... Similar to previous best
## Run 11 stress 9.511959e-05
## ... Procrustes: rmse 9.932362e-05 max resid 0.0002001329
## ... Similar to previous best
## Run 12 stress 8.969811e-05
## ... Procrustes: rmse 0.0001205089 max resid 0.0002574019
## ... Similar to previous best
## Run 13 stress 8.796747e-05
## ... Procrustes: rmse 8.56072e-05 max resid 0.0002052298
## ... Similar to previous best
## Run 14 stress 9.693815e-05
## ... Procrustes: rmse 0.0001012259 max resid 0.0002085296
## ... Similar to previous best
## Run 15 stress 8.442425e-05
## ... Procrustes: rmse 0.0001122393 max resid 0.0002247436
## ... Similar to previous best
## Run 16 stress 9.699694e-05
## ... Procrustes: rmse 0.0001184309 max resid 0.0002528589
## ... Similar to previous best
## Run 17 stress 9.119991e-05
## ... Procrustes: rmse 9.086308e-05 max resid 0.0002544837
## ... Similar to previous best
## Run 18 stress 9.839301e-05
## ... Procrustes: rmse 0.0001046333 max resid 0.0002329734
## ... Similar to previous best
## Run 19 stress 9.280435e-05
## ... Procrustes: rmse 0.0001143868 max resid 0.0002475763
## ... Similar to previous best
## Run 20 stress 8.419121e-05
## ... Procrustes: rmse 6.22314e-05 max resid 0.0001601429
## ... Similar to previous best
## *** Solution reached## Warning in metaMDS(uuni_fecalb, k = 2, trymax = 500): stress is (nearly) zero:
## you may have insufficient data## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
## Run 0 stress 0.1694422
## Run 1 stress 0.1703134
## Run 2 stress 0.1767621
## Run 3 stress 0.1710156
## Run 4 stress 0.1694423
## ... Procrustes: rmse 6.34574e-05 max resid 0.0001699156
## ... Similar to previous best
## Run 5 stress 0.1697768
## ... Procrustes: rmse 0.03161416 max resid 0.08143387
## Run 6 stress 0.1768018
## Run 7 stress 0.1715766
## Run 8 stress 0.1710154
## Run 9 stress 0.203838
## Run 10 stress 0.1767621
## Run 11 stress 0.2174096
## Run 12 stress 0.2133062
## Run 13 stress 0.1767621
## Run 14 stress 0.1694423
## ... Procrustes: rmse 8.271933e-05 max resid 0.0002249821
## ... Similar to previous best
## Run 15 stress 0.1703132
## Run 16 stress 0.1694423
## ... Procrustes: rmse 3.127789e-05 max resid 8.070865e-05
## ... Similar to previous best
## Run 17 stress 0.1767623
## Run 18 stress 0.1710162
## Run 19 stress 0.2038383
## Run 20 stress 0.1710157
## *** Solution reached
tiff(file = "figures/nmds_plot_taxa_uuni_before.tif", res = 150, width = 18,
height = 7, units = "in")
legend1 = get_legend(uunifb + theme(legend.box.margin = margin(0, 0, 0, 12)))## Warning in MASS::cov.trob(data[, vars]): Probable convergence failurecol1 = plot_grid(uunifb + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('A'),
label_size = 20)## Warning in MASS::cov.trob(data[, vars]): Probable convergence failurecol2 = plot_grid(uuniob + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('B'),
label_size = 20)
plot = plot_grid(col1, col2, legend1, nrow = 1, ncol = 3,
rel_widths = c(1.5, 1.5, 0.75), align = "h", axis = "t")
plot
dev.off()## quartz_off_screen
## 2PERMANOVAs - During samples
Filter samples
Filter samples from during the diet intervention and recalculate Bray-Curtis and Jaccard metrics
relab_fecal_filtered_during = relab_fecal_filtered %>%
filter(Study_period == "During")
brayfd = vegdist(relab_fecal_filtered_during[,2:496], method = "bray")
jaccfd = vegdist(relab_fecal_filtered_during[,2:496], method = "jaccard")Bray-Curtis
Fecal
adonis2(brayfd ~ ethnicity, data = relab_fecal_filtered_during,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = brayfd ~ ethnicity, data = relab_fecal_filtered_during, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.7899 0.06703 4.2387 2e-04 ***
## Residual 59 10.9957 0.93297
## Total 60 11.7856 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1NMDS visualization of Bray-Curtis dissimilarity with during samples.
## Run 0 stress 0.1664788
## Run 1 stress 0.1900921
## Run 2 stress 0.2004255
## Run 3 stress 0.1900921
## Run 4 stress 0.2004255
## Run 5 stress 0.2002677
## Run 6 stress 0.2007708
## Run 7 stress 0.1900406
## Run 8 stress 0.1752755
## Run 9 stress 0.1664617
## ... New best solution
## ... Procrustes: rmse 0.001066307 max resid 0.006181484
## ... Similar to previous best
## Run 10 stress 0.1664617
## ... Procrustes: rmse 3.476369e-05 max resid 0.000119953
## ... Similar to previous best
## Run 11 stress 0.2248416
## Run 12 stress 0.1664617
## ... Procrustes: rmse 8.390135e-05 max resid 0.0002723608
## ... Similar to previous best
## Run 13 stress 0.2187408
## Run 14 stress 0.2195501
## Run 15 stress 0.1664617
## ... Procrustes: rmse 8.367231e-06 max resid 2.661781e-05
## ... Similar to previous best
## Run 16 stress 0.208741
## Run 17 stress 0.1664788
## ... Procrustes: rmse 0.001068306 max resid 0.006189377
## ... Similar to previous best
## Run 18 stress 0.2003321
## Run 19 stress 0.1900406
## Run 20 stress 0.2266991
## *** Solution reached
tiff(file = "figures/nmds_plot_taxa_bray_during.tif", res = 150, width = 11,
height = 7, units = "in")
brayfd
dev.off()## quartz_off_screen
## 2Jaccard
Fecal
adonis2(jaccfd ~ ethnicity, data = relab_fecal_filtered_during,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = jaccfd ~ ethnicity, data = relab_fecal_filtered_during, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 1.0741 0.06259 3.9392 4e-04 ***
## Residual 59 16.0879 0.93741
## Total 60 17.1620 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1NMDS visualization of Jaccard similarity with during samples.
## Run 0 stress 0.1664788
## Run 1 stress 0.172702
## Run 2 stress 0.223669
## Run 3 stress 0.1664788
## ... New best solution
## ... Procrustes: rmse 4.915646e-05 max resid 0.0001542918
## ... Similar to previous best
## Run 4 stress 0.1965621
## Run 5 stress 0.19664
## Run 6 stress 0.1664617
## ... New best solution
## ... Procrustes: rmse 0.001068985 max resid 0.006190718
## ... Similar to previous best
## Run 7 stress 0.2008191
## Run 8 stress 0.1900394
## Run 9 stress 0.1664617
## ... Procrustes: rmse 6.804985e-05 max resid 0.0002170154
## ... Similar to previous best
## Run 10 stress 0.2007708
## Run 11 stress 0.2004655
## Run 12 stress 0.1784168
## Run 13 stress 0.2039778
## Run 14 stress 0.1724188
## Run 15 stress 0.1664617
## ... Procrustes: rmse 7.791776e-06 max resid 2.582068e-05
## ... Similar to previous best
## Run 16 stress 0.1727032
## Run 17 stress 0.1900973
## Run 18 stress 0.1664617
## ... Procrustes: rmse 1.324225e-05 max resid 4.279012e-05
## ... Similar to previous best
## Run 19 stress 0.2039778
## Run 20 stress 0.1998369
## *** Solution reached
tiff(file = "figures/nmds_plot_taxa_jacc_during.tif", res = 150, width = 11,
height = 7, units = "in")
jaccfd
dev.off()## quartz_off_screen
## 2Weighted UniFrac
Fecal
adonis2(wuni_fecald ~ ethnicity, data = relab_fecal_filtered_during,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = wuni_fecald ~ ethnicity, data = relab_fecal_filtered_during, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.15963 0.06033 3.7881 0.0134 *
## Residual 59 2.48636 0.93967
## Total 60 2.64599 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1NMDS visualization of weighted Unifrac distances with during samples.
## Run 0 stress 0.1065766
## Run 1 stress 0.1066173
## ... Procrustes: rmse 0.004441149 max resid 0.02908068
## Run 2 stress 0.121613
## Run 3 stress 0.1170402
## Run 4 stress 0.1347412
## Run 5 stress 0.1066765
## ... Procrustes: rmse 0.0117531 max resid 0.08209334
## Run 6 stress 0.1328284
## Run 7 stress 0.110359
## Run 8 stress 0.1157495
## Run 9 stress 0.110029
## Run 10 stress 0.1421085
## Run 11 stress 0.1171166
## Run 12 stress 0.1065766
## ... New best solution
## ... Procrustes: rmse 5.599846e-05 max resid 0.0002443808
## ... Similar to previous best
## Run 13 stress 0.1159533
## Run 14 stress 0.1066172
## ... Procrustes: rmse 0.004443165 max resid 0.02906563
## Run 15 stress 0.1381071
## Run 16 stress 0.1378626
## Run 17 stress 0.1157515
## Run 18 stress 0.138338
## Run 19 stress 0.1182267
## Run 20 stress 0.1125892
## *** Solution reached
tiff(file = "figures/nmds_plot_taxa_wuni_during.tif", res = 150, width = 11,
height = 7, units = "in")
wunif
dev.off()## quartz_off_screen
## 2Unweighted UniFrac
Fecal
adonis2(uuni_fecald ~ ethnicity, data = relab_fecal_filtered_during,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = uuni_fecald ~ ethnicity, data = relab_fecal_filtered_during, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.4436 0.07779 4.9764 2e-04 ***
## Residual 59 5.2595 0.92221
## Total 60 5.7031 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1NMDS visualization of unweighted Unifrac distance with during samples.
## Run 0 stress 0.1793941
## Run 1 stress 0.1815779
## Run 2 stress 0.1793928
## ... New best solution
## ... Procrustes: rmse 0.001474469 max resid 0.008378042
## ... Similar to previous best
## Run 3 stress 0.1799881
## Run 4 stress 0.1800008
## Run 5 stress 0.1794325
## ... Procrustes: rmse 0.005388824 max resid 0.02821562
## Run 6 stress 0.1794325
## ... Procrustes: rmse 0.00536919 max resid 0.02815032
## Run 7 stress 0.2121887
## Run 8 stress 0.1866832
## Run 9 stress 0.1799881
## Run 10 stress 0.179654
## ... Procrustes: rmse 0.01012475 max resid 0.03493015
## Run 11 stress 0.1847463
## Run 12 stress 0.1813386
## Run 13 stress 0.1793933
## ... Procrustes: rmse 0.001043957 max resid 0.005966139
## ... Similar to previous best
## Run 14 stress 0.1796595
## ... Procrustes: rmse 0.01082075 max resid 0.03796503
## Run 15 stress 0.2119015
## Run 16 stress 0.2120047
## Run 17 stress 0.2184909
## Run 18 stress 0.1800008
## Run 19 stress 0.2171491
## Run 20 stress 0.1794952
## ... Procrustes: rmse 0.007252083 max resid 0.03211737
## *** Solution reached
tiff(file = "figures/nmds_plot_taxa_uuni_during.tif", res = 150, width = 18,
height = 7, units = "in")
uunifd
dev.off()## quartz_off_screen
## 2PERMANOVAs - After samples
Filter samples
Filter after samples and recalculate Bray-Curtis and Jaccard metrics
relab_fecal_filtered_after = relab_fecal_filtered %>%
filter(Study_period == "After")
relab_oral_after = relab_oral %>%
filter(Study_period == "After")
brayfa = vegdist(relab_fecal_filtered_after[,2:496], method = "bray")
jaccfa = vegdist(relab_fecal_filtered_after[,2:496], method = "jaccard")
brayoa = vegdist(relab_oral_after[,2:496], method = "bray")
jaccoa = vegdist(relab_oral_after[,2:496], method = "jaccard")Bray-Curtis
Fecal
adonis2(brayfa ~ ethnicity, data = relab_fecal_filtered_after,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = brayfa ~ ethnicity, data = relab_fecal_filtered_after, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.4762 0.07579 2.3782 0.0208 *
## Residual 29 5.8065 0.92421
## Total 30 6.2827 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Oral
adonis2(brayoa ~ ethnicity, data = relab_oral_after,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = brayoa ~ ethnicity, data = relab_oral_after, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.24789 0.14292 2.5014 0.018 *
## Residual 15 1.48655 0.85708
## Total 16 1.73444 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1NMDS visualization of Bray-Curtis dissimilarity with after samples.
## Run 0 stress 0.1590347
## Run 1 stress 0.1632431
## Run 2 stress 0.1711856
## Run 3 stress 0.1912912
## Run 4 stress 0.1590551
## ... Procrustes: rmse 0.004813471 max resid 0.01859784
## Run 5 stress 0.1909684
## Run 6 stress 0.1632431
## Run 7 stress 0.1590347
## ... New best solution
## ... Procrustes: rmse 2.462918e-05 max resid 7.695649e-05
## ... Similar to previous best
## Run 8 stress 0.2009304
## Run 9 stress 0.1916395
## Run 10 stress 0.1913422
## Run 11 stress 0.1590347
## ... New best solution
## ... Procrustes: rmse 1.255126e-05 max resid 3.775422e-05
## ... Similar to previous best
## Run 12 stress 0.1590347
## ... Procrustes: rmse 1.23226e-05 max resid 5.312786e-05
## ... Similar to previous best
## Run 13 stress 0.2053616
## Run 14 stress 0.1885132
## Run 15 stress 0.1904057
## Run 16 stress 0.1802135
## Run 17 stress 0.1656964
## Run 18 stress 0.2359138
## Run 19 stress 0.1590347
## ... Procrustes: rmse 1.633959e-05 max resid 5.295247e-05
## ... Similar to previous best
## Run 20 stress 0.1632431
## *** Solution reached
## Run 0 stress 0.1367903
## Run 1 stress 0.1367906
## ... Procrustes: rmse 0.0001540849 max resid 0.0003625897
## ... Similar to previous best
## Run 2 stress 0.1345672
## ... New best solution
## ... Procrustes: rmse 0.03762177 max resid 0.1126176
## Run 3 stress 0.1345672
## ... New best solution
## ... Procrustes: rmse 7.113183e-06 max resid 1.54181e-05
## ... Similar to previous best
## Run 4 stress 0.1367906
## Run 5 stress 0.1573084
## Run 6 stress 0.1367904
## Run 7 stress 0.1367904
## Run 8 stress 0.1520581
## Run 9 stress 0.1367902
## Run 10 stress 0.1345672
## ... New best solution
## ... Procrustes: rmse 2.539426e-06 max resid 4.973324e-06
## ... Similar to previous best
## Run 11 stress 0.1563232
## Run 12 stress 0.2845416
## Run 13 stress 0.1563232
## Run 14 stress 0.1504065
## Run 15 stress 0.1367904
## Run 16 stress 0.1345672
## ... Procrustes: rmse 1.34276e-05 max resid 2.875464e-05
## ... Similar to previous best
## Run 17 stress 0.1504065
## Run 18 stress 0.1367902
## Run 19 stress 0.1769399
## Run 20 stress 0.1577923
## *** Solution reached
tiff(file = "figures/nmds_plot_taxa_bray_after.tif", res = 150, width = 18,
height = 7, units = "in")
legend1 = get_legend(brayfa + theme(legend.box.margin = margin(0, 0, 0, 12)))
col1 = plot_grid(brayfa + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('A'),
label_size = 20)
col2 = plot_grid(brayoa + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('B'),
label_size = 20)
plot = plot_grid(col1, col2, legend1, nrow = 1, ncol = 3,
rel_widths = c(1.5, 1.5, 0.75), align = "h", axis = "t")
plot
dev.off()## quartz_off_screen
## 2Jaccard
Fecal
adonis2(jaccfa ~ ethnicity, data = relab_fecal_filtered_after,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = jaccfa ~ ethnicity, data = relab_fecal_filtered_after, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.6328 0.07096 2.2149 0.0144 *
## Residual 29 8.2848 0.92904
## Total 30 8.9175 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Oral
adonis2(jaccoa ~ ethnicity, data = relab_oral_after,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = jaccoa ~ ethnicity, data = relab_oral_after, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.36814 0.11723 1.9919 0.0168 *
## Residual 15 2.77222 0.88277
## Total 16 3.14036 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1NMDS visualization of Jaccard similarity with after samples.
## Run 0 stress 0.1590347
## Run 1 stress 0.1776623
## Run 2 stress 0.3892089
## Run 3 stress 0.1966504
## Run 4 stress 0.2027067
## Run 5 stress 0.159055
## ... Procrustes: rmse 0.004810536 max resid 0.01859397
## Run 6 stress 0.1590554
## ... Procrustes: rmse 0.0048899 max resid 0.0188668
## Run 7 stress 0.1590547
## ... Procrustes: rmse 0.004675963 max resid 0.01804715
## Run 8 stress 0.1773823
## Run 9 stress 0.2218271
## Run 10 stress 0.2014925
## Run 11 stress 0.1909684
## Run 12 stress 0.1590347
## ... Procrustes: rmse 2.903439e-05 max resid 8.579623e-05
## ... Similar to previous best
## Run 13 stress 0.1590347
## ... Procrustes: rmse 2.174493e-05 max resid 8.522461e-05
## ... Similar to previous best
## Run 14 stress 0.1590348
## ... Procrustes: rmse 0.0001101951 max resid 0.0002671833
## ... Similar to previous best
## Run 15 stress 0.1866588
## Run 16 stress 0.1878211
## Run 17 stress 0.2009304
## Run 18 stress 0.1978081
## Run 19 stress 0.1590347
## ... Procrustes: rmse 1.189067e-05 max resid 2.539754e-05
## ... Similar to previous best
## Run 20 stress 0.1632431
## *** Solution reached
## Run 0 stress 0.1367902
## Run 1 stress 0.1345672
## ... New best solution
## ... Procrustes: rmse 0.03748147 max resid 0.1125011
## Run 2 stress 0.1520582
## Run 3 stress 0.1520583
## Run 4 stress 0.1367902
## Run 5 stress 0.1345672
## ... Procrustes: rmse 2.018597e-05 max resid 4.552282e-05
## ... Similar to previous best
## Run 6 stress 0.1769413
## Run 7 stress 0.1504065
## Run 8 stress 0.1367902
## Run 9 stress 0.1504065
## Run 10 stress 0.1563232
## Run 11 stress 0.1345672
## ... Procrustes: rmse 1.494667e-05 max resid 3.577065e-05
## ... Similar to previous best
## Run 12 stress 0.1345672
## ... Procrustes: rmse 2.801263e-05 max resid 6.683795e-05
## ... Similar to previous best
## Run 13 stress 0.1504066
## Run 14 stress 0.1367903
## Run 15 stress 0.1345672
## ... Procrustes: rmse 3.525083e-05 max resid 9.642475e-05
## ... Similar to previous best
## Run 16 stress 0.1345672
## ... Procrustes: rmse 2.232049e-05 max resid 4.431062e-05
## ... Similar to previous best
## Run 17 stress 0.1563232
## Run 18 stress 0.1367905
## Run 19 stress 0.1637332
## Run 20 stress 0.1367905
## *** Solution reached
tiff(file = "figures/nmds_plot_taxa_jacc_after.tif", res = 150, width = 18,
height = 7, units = "in")
legend1 = get_legend(jaccfa + theme(legend.box.margin = margin(0, 0, 0, 12)))
col1 = plot_grid(jaccfa + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('A'),
label_size = 20)
col2 = plot_grid(jaccoa + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('B'),
label_size = 20)
plot = plot_grid(col1, col2, legend1, nrow = 1, ncol = 3,
rel_widths = c(1.5, 1.5, 0.75), align = "h", axis = "t")
plot
dev.off()## quartz_off_screen
## 2Weighted UniFrac
Fecal
adonis2(wuni_fecala ~ ethnicity, data = relab_fecal_filtered_after,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = wuni_fecala ~ ethnicity, data = relab_fecal_filtered_after, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.09394 0.07012 2.1868 0.0766 .
## Residual 29 1.24577 0.92988
## Total 30 1.33971 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Oral
adonis2(wuni_orala ~ ethnicity, data = relab_oral_after,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = wuni_orala ~ ethnicity, data = relab_oral_after, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.07966 0.11412 1.9323 0.1274
## Residual 15 0.61840 0.88588
## Total 16 0.69806 1.00000NMDS visualization of weighted Unifrac distances with after samples.
## Run 0 stress 0.103874
## Run 1 stress 0.1259496
## Run 2 stress 0.1030343
## ... New best solution
## ... Procrustes: rmse 0.03047841 max resid 0.1470687
## Run 3 stress 0.1079067
## Run 4 stress 0.1030343
## ... Procrustes: rmse 7.058277e-05 max resid 0.0002370727
## ... Similar to previous best
## Run 5 stress 0.1030343
## ... New best solution
## ... Procrustes: rmse 1.996087e-05 max resid 6.624552e-05
## ... Similar to previous best
## Run 6 stress 0.1038765
## Run 7 stress 0.1147664
## Run 8 stress 0.1079067
## Run 9 stress 0.1079067
## Run 10 stress 0.112468
## Run 11 stress 0.1038764
## Run 12 stress 0.1079067
## Run 13 stress 0.3906131
## Run 14 stress 0.1038693
## Run 15 stress 0.1030343
## ... Procrustes: rmse 1.588753e-05 max resid 5.537614e-05
## ... Similar to previous best
## Run 16 stress 0.1283419
## Run 17 stress 0.1030343
## ... Procrustes: rmse 6.011413e-05 max resid 0.0002865742
## ... Similar to previous best
## Run 18 stress 0.1030343
## ... New best solution
## ... Procrustes: rmse 4.031004e-06 max resid 1.078038e-05
## ... Similar to previous best
## Run 19 stress 0.1030343
## ... New best solution
## ... Procrustes: rmse 2.101056e-05 max resid 9.684372e-05
## ... Similar to previous best
## Run 20 stress 0.1038692
## *** Solution reached
## Run 0 stress 0.05926746
## Run 1 stress 0.09412422
## Run 2 stress 0.07447768
## Run 3 stress 0.08351463
## Run 4 stress 0.0866882
## Run 5 stress 0.09687134
## Run 6 stress 0.07191275
## Run 7 stress 0.05926746
## ... Procrustes: rmse 1.264535e-06 max resid 3.067386e-06
## ... Similar to previous best
## Run 8 stress 0.07401827
## Run 9 stress 0.0866882
## Run 10 stress 0.08521648
## Run 11 stress 0.07447768
## Run 12 stress 0.08726034
## Run 13 stress 0.08605518
## Run 14 stress 0.07191278
## Run 15 stress 0.07447768
## Run 16 stress 0.05926746
## ... Procrustes: rmse 2.169967e-05 max resid 6.844801e-05
## ... Similar to previous best
## Run 17 stress 0.05926746
## ... New best solution
## ... Procrustes: rmse 6.442793e-06 max resid 1.983388e-05
## ... Similar to previous best
## Run 18 stress 0.05926746
## ... Procrustes: rmse 5.890497e-06 max resid 1.692085e-05
## ... Similar to previous best
## Run 19 stress 0.09687134
## Run 20 stress 0.07191276
## *** Solution reached
tiff(file = "figures/nmds_plot_taxa_wuni_after.tif", res = 150, width = 18,
height = 7, units = "in")
legend1 = get_legend(wunifa + theme(legend.box.margin = margin(0, 0, 0, 12)))
col1 = plot_grid(wunifa + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('A'),
label_size = 20)
col2 = plot_grid(wunioa + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('B'),
label_size = 20)
plot = plot_grid(col1, col2, legend1, nrow = 1, ncol = 3,
rel_widths = c(1.5, 1.5, 0.75), align = "h", axis = "t")
plot
dev.off()## quartz_off_screen
## 2Unweighted UniFrac
Fecal
adonis2(uuni_fecala ~ ethnicity, data = relab_fecal_filtered_after,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = uuni_fecala ~ ethnicity, data = relab_fecal_filtered_after, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.20279 0.07361 2.3043 0.0052 **
## Residual 29 2.55215 0.92639
## Total 30 2.75494 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Oral
adonis2(uuni_orala ~ ethnicity, data = relab_oral_after,
permutations = 4999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 4999
##
## adonis2(formula = uuni_orala ~ ethnicity, data = relab_oral_after, permutations = 4999)
## Df SumOfSqs R2 F Pr(>F)
## ethnicity 1 0.04386 0.06782 1.0914 0.3476
## Residual 15 0.60285 0.93218
## Total 16 0.64671 1.00000NMDS visualization of unweighted UniFrac distances with after samples.
## Run 0 stress 0.1657305
## Run 1 stress 0.1658233
## ... Procrustes: rmse 0.00537419 max resid 0.02533288
## Run 2 stress 0.1833738
## Run 3 stress 0.2137625
## Run 4 stress 0.1789747
## Run 5 stress 0.1781267
## Run 6 stress 0.1686699
## Run 7 stress 0.1678211
## Run 8 stress 0.1681404
## Run 9 stress 0.1824032
## Run 10 stress 0.1657192
## ... New best solution
## ... Procrustes: rmse 0.001665992 max resid 0.005852336
## ... Similar to previous best
## Run 11 stress 0.1681404
## Run 12 stress 0.1675407
## Run 13 stress 0.1657194
## ... Procrustes: rmse 0.0001596051 max resid 0.0007250493
## ... Similar to previous best
## Run 14 stress 0.1686699
## Run 15 stress 0.1835957
## Run 16 stress 0.1658235
## ... Procrustes: rmse 0.00589096 max resid 0.02558163
## Run 17 stress 0.1686699
## Run 18 stress 0.1789746
## Run 19 stress 0.1675406
## Run 20 stress 0.1657189
## ... New best solution
## ... Procrustes: rmse 0.0003874098 max resid 0.001768522
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.1364074
## Run 1 stress 0.1694069
## Run 2 stress 0.1442196
## Run 3 stress 0.1442203
## Run 4 stress 0.1304387
## ... New best solution
## ... Procrustes: rmse 0.04532598 max resid 0.1276811
## Run 5 stress 0.1380133
## Run 6 stress 0.1304387
## ... New best solution
## ... Procrustes: rmse 3.720389e-05 max resid 0.0001027526
## ... Similar to previous best
## Run 7 stress 0.1543537
## Run 8 stress 0.136732
## Run 9 stress 0.1304387
## ... Procrustes: rmse 1.198968e-05 max resid 3.044183e-05
## ... Similar to previous best
## Run 10 stress 0.1670458
## Run 11 stress 0.1439974
## Run 12 stress 0.1442198
## Run 13 stress 0.1367313
## Run 14 stress 0.1384723
## Run 15 stress 0.1304386
## ... New best solution
## ... Procrustes: rmse 8.04969e-05 max resid 0.0002287135
## ... Similar to previous best
## Run 16 stress 0.1442208
## Run 17 stress 0.1442203
## Run 18 stress 0.1679828
## Run 19 stress 0.1445522
## Run 20 stress 0.1571375
## *** Solution reached
tiff(file = "figures/nmds_plot_taxa_uuni_after.tif", res = 150, width = 18,
height = 7, units = "in")
legend1 = get_legend(uunifa + theme(legend.box.margin = margin(0, 0, 0, 12)))
col1 = plot_grid(uunifa + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('A'),
label_size = 20)
col2 = plot_grid(uunioa + theme(legend.position = "none"),
nrow = 1, ncol = 1, labels = c('B'),
label_size = 20)
plot = plot_grid(col1, col2, legend1, nrow = 1, ncol = 3,
rel_widths = c(1.5, 1.5, 0.75), align = "h", axis = "t")
plot
dev.off()## quartz_off_screen
## 2